Quick Commerce API for Market Intelligence: Track Inventory & Pricing Trends

India's quick commerce market crossed $5 billion in GMV in 2025, and it is growing at over 40% year-over-year. For D2C brands, FMCG companies, and market research firms, understanding what is happening across these platforms in real-time is no longer optional. Which platform is discounting your competitor's product? Where is your product out of stock? How do prices shift during Diwali versus a regular Tuesday? These are the questions that separate data-driven brands from those flying blind.

The QuickCommerce API gives you programmatic access to pricing, availability, and product data across 7 major platforms. In this guide, we will show you how to build a market intelligence pipeline that tracks pricing trends, monitors inventory levels, and generates competitive insights. Whether you are a brand manager at an FMCG company or a data engineer at a market research firm, this approach scales from tracking a handful of SKUs to thousands.

Platforms Covered

The API covers all major quick commerce platforms operating in India. Each platform has different pricing strategies, inventory management approaches, and promotional calendars. Tracking all 7 gives you a comprehensive view of the market. Visit individual platform pages for platform-specific data fields and capabilities.

BlinkItZeptoSwiggyBigBasketDMartJioMartMinutes

Tracking Pricing Trends with the Search Endpoint

The search endpoint is your primary tool for price discovery. It returns the current selling price, MRP, and any offer price for products on a given platform. By querying at regular intervals, you build a pricing time-series that reveals trends, seasonal patterns, and competitive moves. The search endpoint costs just 1 credit per call, making it the most cost-effective way to gather pricing data at scale.

Example: Tracking Coca-Cola Pricing on BigBasket

curl -X GET "https://api.quickcommerceapi.com/api/v1/search?query=Coca+Cola+2L&platform=bigbasket" \
  -H "X-API-Key: YOUR_API_KEY"

{
  "query": "Coca Cola 2L",
  "platform": "bigbasket",
  "products": [
    {
      "name": "Coca-Cola Soft Drink - Original Taste, 2.25 L Bottle",
      "price": 87,
      "mrp": 95,
      "offer_price": 87,
      "image": "https://www.bigbasket.com/coca-cola-2l.jpg",
      "in_stock": true,
      "quantity": "2.25 L",
      "brand": "Coca-Cola",
      "category": "Beverages",
      "sub_category": "Soft Drinks"
    },
    {
      "name": "Coca-Cola Soft Drink, 2 L Bottle",
      "price": 78,
      "mrp": 86,
      "offer_price": 78,
      "image": "https://www.bigbasket.com/coca-cola-2lb.jpg",
      "in_stock": true,
      "quantity": "2 L",
      "brand": "Coca-Cola",
      "category": "Beverages",
      "sub_category": "Soft Drinks"
    }
  ],
  "total_results": 2,
  "credits_used": 1,
  "response_time_ms": 823
}

Monitoring Inventory and Availability

Inventory monitoring is critical for brands that sell through quick commerce. If your product goes out of stock on BlinkIt in Mumbai but remains available on Zepto, you are losing sales. The search endpoint's in_stock field tells you whether a product is currently available. For more detailed inventory data on a specific product, use the item endpoint which returns additional fields like inventory count and stock status.

Example: Checking Specific SKU Availability

curl -X GET "https://api.quickcommerceapi.com/api/v1/item?url=https://blinkit.com/prn/coca-cola-soft-drink/prid/123456&platform=blinkit" \
  -H "X-API-Key: YOUR_API_KEY"

{
  "platform": "blinkit",
  "product": {
    "name": "Coca-Cola Soft Drink 2 L",
    "price": 82,
    "mrp": 86,
    "offer_price": 82,
    "image": "https://cdn.blinkit.com/coca-cola-2l.jpg",
    "in_stock": true,
    "quantity": "2 L",
    "brand": "Coca-Cola",
    "category": "Soft Drinks & Juices",
    "sub_category": "Soft Drinks",
    "description": "Coca-Cola Original Taste, 2L PET Bottle",
    "inventory": {
      "count": 47,
      "status": "in_stock",
      "low_stock": false
    },
    "ratings": {
      "average": 4.3,
      "count": 1247
    }
  },
  "credits_used": 1,
  "response_time_ms": 654
}

Cross-Platform Pricing and Availability

Here is a snapshot of Coca-Cola 2L pricing and availability across 6 platforms in Mumbai. Notice how prices differ by up to Rs 12 for the exact same product. DMart offers the lowest price through its DMart Ready platform, but with longer delivery times. This is the kind of competitive intelligence that helps brands and retailers make data-driven decisions.

Building a Competitive Intelligence Dashboard

Let us put it all together with a Python script that tracks competitor products across platforms daily. This script queries multiple products on multiple platforms, stores the data in a structured format, and generates a daily report. This is the foundation of a market intelligence pipeline that can feed into BI tools like Metabase, Tableau, or custom dashboards.

import requests
import sqlite3
import json
from datetime import datetime
from typing import Dict, List

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.quickcommerceapi.com/api/v1"

# Products to track (your brand + competitors)
TRACKING_LIST = [
    {"query": "Coca Cola 2L", "brand": "Coca-Cola", "category": "Beverages"},
    {"query": "Pepsi 2L", "brand": "PepsiCo", "category": "Beverages"},
    {"query": "Thums Up 2L", "brand": "Coca-Cola", "category": "Beverages"},
    {"query": "Sprite 2L", "brand": "Coca-Cola", "category": "Beverages"},
    {"query": "Mountain Dew 2L", "brand": "PepsiCo", "category": "Beverages"},
]

PLATFORMS = ["blinkit", "zepto", "swiggy", "bigbasket", "dmart", "jiomart"]
PINCODES = {
    "Mumbai": "400053",
    "Bangalore": "560034",
    "Delhi": "110001",
}

# Initialize database
db = sqlite3.connect("market_intelligence.db")
db.execute("""
    CREATE TABLE IF NOT EXISTS price_data (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        query TEXT, brand TEXT, category TEXT,
        platform TEXT, city TEXT, pincode TEXT,
        product_name TEXT, price REAL, mrp REAL,
        offer_price REAL, in_stock INTEGER,
        tracked_at DATETIME DEFAULT CURRENT_TIMESTAMP
    )
""")

def search_product(query: str, platform: str, pincode: str) -> Dict:
    """Search for a product on a specific platform."""
    headers = {
        "X-API-Key": API_KEY,
        "x-geolocation-pincode": pincode,
    }
    params = {"query": query, "platform": platform}
    resp = requests.get(f"{BASE_URL}/search", headers=headers, params=params)
    return resp.json()

def track_all():
    """Run a full tracking cycle across all products, platforms, and cities."""
    timestamp = datetime.now().isoformat()
    total_tracked = 0

    for city, pincode in PINCODES.items():
        for item in TRACKING_LIST:
            for platform in PLATFORMS:
                try:
                    data = search_product(item["query"], platform, pincode)
                    product = data.get("products", [None])[0]
                    if product:
                        db.execute(
                            """INSERT INTO price_data
                            (query, brand, category, platform, city, pincode,
                             product_name, price, mrp, offer_price, in_stock)
                            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
                            (
                                item["query"], item["brand"], item["category"],
                                platform, city, pincode,
                                product["name"], product["price"],
                                product["mrp"], product.get("offer_price"),
                                1 if product["in_stock"] else 0,
                            ),
                        )
                        total_tracked += 1
                except Exception as e:
                    print(f"[ERR] {platform}/{city}: {item['query']} - {e}")

                # Rate limit: 500ms between calls
                import time; time.sleep(0.5)

    db.commit()
    print(f"[{timestamp}] Tracked {total_tracked} data points")

def generate_daily_report() -> str:
    """Generate a summary of today's pricing data."""
    cursor = db.execute("""
        SELECT brand, platform, city,
               AVG(price) as avg_price,
               MIN(price) as min_price,
               MAX(price) as max_price,
               SUM(CASE WHEN in_stock = 0 THEN 1 ELSE 0 END) as oos_count,
               COUNT(*) as total
        FROM price_data
        WHERE DATE(tracked_at) = DATE('now')
        GROUP BY brand, platform, city
        ORDER BY brand, platform, city
    """)

    report = "=== Daily Market Intelligence Report ===\n"
    for row in cursor.fetchall():
        brand, platform, city, avg_p, min_p, max_p, oos, total = row
        report += (
            f"{brand} | {platform:10s} | {city:10s} | "
            f"Avg: Rs {avg_p:.0f} | Range: Rs {min_p:.0f}-{max_p:.0f} | "
            f"OOS: {oos}/{total}\n"
        )
    return report

if __name__ == "__main__":
    track_all()
    print(generate_daily_report())

Identifying Pricing Patterns

Once you have a few weeks of pricing data, patterns start to emerge. Festive seasons like Diwali and Holi bring steep discounts as platforms compete for orders. Weekend prices on some platforms spike slightly due to higher demand. And platform-specific sale events like BigBasket's "BB Star" days or BlinkIt's "Mania" sales create temporary price drops that competitors sometimes match.

Here are some patterns our users have discovered by tracking prices over time with the QuickCommerce API:

Common Pricing Patterns in Indian Quick Commerce

Data-Driven Decisions for D2C Brands

If you are a D2C brand selling through quick commerce channels, this data is gold. You can see in real-time how competitors are pricing similar products, where your products go out of stock, and which platforms are discounting your products the most. This helps you negotiate better with platform category managers, optimize your pricing strategy, and ensure you never lose sales due to stockouts.

For FMCG companies, the intelligence goes even deeper. Track how your product portfolio performs against competitors across regions. Identify cities where a competitor is gaining share through aggressive pricing. Spot distribution gaps where your product is listed on only 3 out of 7 platforms. The QuickCommerce API provides the raw data to power these insights.

Setting Up Your Intelligence Pipeline

Scaling Your Intelligence Operation

For enterprise use cases tracking thousands of SKUs, you will want to optimize your credit usage. Use the search endpoint for broad monitoring at 1 credit per call. Reserve the item endpoint for deep dives on specific products. Use groupsearch when you need to compare prices across platforms simultaneously. And check our pricing page for volume discounts on bulk credit packs.

Here is a rough credit budget for different scales of market intelligence operations:

Get Started Today

Market intelligence in quick commerce is a competitive advantage that compounds over time. The sooner you start collecting data, the richer your historical insights become. Sign up for free, test the API in the Playground, and start building your intelligence pipeline today. For questions about enterprise use cases, reach out to us directly.

For more on tracking specific products, see our grocery price tracking guide. To monitor product availability specifically, check out our availability monitoring guide. And for real-time price comparison apps, start with our price comparison tutorial.